This application claims the priority of Korean Patent Application No. 2003-82639, filed on Nov. 20, 2003, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.
1. Field of the Invention
The present invention is related to a system and method for processing a human body image, and more particularly, to develop a semi-automated system for human image processing with which tissues or organs from human images can be segmented and classified by people who have basic knowledge of image processing. In addition, the proposed image processing system is independent on any types of human tissues or images.
2. Description of the Related Art
A high resolution human model is necessary in order to analyze electromagnetic field (EMF) effects exposed on human body by telecommunication devices. A high resolution human model can be developed by segmenting human images such as magnetic resonance imaging (MRI) images and computed tomography (CT) images. The segmented images can be reconstructed to a 3D model with information of anatomical structures.
Although segmentation of meaningful tissues in the MRI or CT images is a must task, the MRI or CT images cannot show all the tissues of a human body due to their unique characteristics. As a result, there is no program for automatic and perfect segmentation.
In general, segmentation of MRI or CT images for constructing the high resolution human body model is done manually such that regions to be segmented are drawn by hand on MRI or CT images and the resulting images are input to a computer using a general-purpose graphic software.
Manual segmentation offers reliable results but it is very labor- and time-intensive. In particular, the results of segmentation significantly differ depending on skills of those who perform segmentation. In other words, since tissues in every image should be checked with eyes and edges of the tissues should be drawn by hand, manual segmentation requires a high level of knowledge and experience and inevitably depends on experts.
Also, since manual segmentation requires high concentration, even experts cannot handle a limited amount of tasks in a day. When the general-purpose image processing software is used, it often does not provide user's desired functions or is very inconvenient for specific purposes.
Although a fully automated image processing system using computer algorithms exists, its operation is limited to processing of images of specific tissues such as brains, stomachs, or livers, or it should use a specific image like T1-weighted MR images or T2-weighted MR images that emphasize a specific portion of a human body. An automatic image processing system can save user's time and effort, but the accuracy of processing results is low. Such a limit of the automatic image processing system can be overcome by a semi-automatic image processing system that is a product of combination of advantages of manual and automatic image processing systems.
Thus, there is an urgent requirement for development of an image processing system that has the advantage of saving user's time and effort and functions required for segmentation of MRI and CT images and is independent of specific tissues of a human body or specific images.